{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    }
   ],
   "source": [
    "%matplotlib qt\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn import datasets\n",
    "from sklearn.decomposition import PCA\n",
    "from prettytable import PrettyTable\n",
    "from sklearn.decomposition import KernelPCA as kpca\n",
    "from sklearn.metrics import roc_curve, auc,roc_auc_score,f1_score,confusion_matrix\n",
    "import numpy as np\n",
    "print (__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "filename=\"credit.csv\"\n",
    "f = open(filename)\n",
    "f.readline()  # skip the header\n",
    "data =  np.loadtxt(fname = f, delimiter = ',',dtype='double')\n",
    "Y = data[:,data.shape[1]-1]\n",
    "X = data[:, 0:data.shape[1]-1] \n",
    "\n",
    "# Reading the labels now\n",
    "f= open(filename)\n",
    "labels_=np.loadtxt(fname=f,delimiter=',',dtype='string')\n",
    "labels_=labels_[0,0:data.shape[1]-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X:  [[  1.   6.   4. ...,   1.   2.   1.]\n",
      " [  2.  48.   2. ...,   1.   1.   1.]\n",
      " [  4.  12.   4. ...,   2.   1.   1.]\n",
      " ..., \n",
      " [  4.  12.   2. ...,   1.   1.   1.]\n",
      " [  1.  45.   2. ...,   1.   2.   1.]\n",
      " [  2.  45.   4. ...,   1.   1.   1.]]\n",
      "Y:  [ 1.  2.  1.  1.  2.  1.  1.  1.  1.  2.  2.  2.  1.  2.  1.  2.  1.  1.\n",
      "  2.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  2.  1.  1.  1.  1.  1.  2.\n",
      "  1.  2.  1.  1.  1.  1.  1.  1.  2.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "  2.  1.  2.  1.  1.  2.  1.  1.  2.  2.  1.  1.  1.  1.  2.  1.  1.  1.\n",
      "  1.  1.  2.  1.  2.  1.  1.  1.  2.  1.  1.  1.  1.  1.  1.  2.  1.  2.\n",
      "  1.  1.  2.  1.  1.  2.  1.  1.  1.  1.  1.  1.  1.  1.  1.  2.  2.  1.\n",
      "  1.  1.  1.  1.  1.  2.  1.  1.  2.  1.  2.  1.  2.  1.  1.  1.  2.  1.\n",
      "  1.  2.  1.  2.  1.  2.  1.  1.  1.  1.  1.  2.  1.  1.  1.  1.  1.  2.\n",
      "  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  2.  1.  1.  1.  1.  1.  1.\n",
      "  1.  1.  1.  1.  2.  1.  1.  2.  2.  1.  2.  1.  2.  2.  1.  1.  1.  1.\n",
      "  2.  2.  2.  1.  2.  1.  2.  1.  2.  1.  2.  2.  2.  1.  2.  2.  1.  2.\n",
      "  1.  2.  1.  1.  1.  2.  1.  1.  1.  1.  1.  1.  1.  1.  2.  2.  1.  1.\n",
      "  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  2.  2.  2.  1.  2.  1.  1.  1.\n",
      "  1.  2.  2.  2.  1.  1.  2.  1.  2.  1.  1.  1.  1.  1.  1.  2.  1.  1.\n",
      "  2.  1.  1.  1.  1.  2.  1.  1.  1.  1.  1.  1.  1.  2.  1.  1.  2.  1.\n",
      "  1.  1.  1.  2.  2.  1.  1.  1.  2.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "  1.  2.  1.  2.  1.  1.  1.  2.  1.  1.  1.  1.  1.  2.  2.  1.  2.  1.\n",
      "  1.  2.  2.  1.  1.  1.  1.  2.  1.  2.  1.  1.  1.  1.  2.  2.  1.  1.\n",
      "  1.  1.  1.  1.  1.  1.  1.  2.  2.  2.  2.  2.  1.  2.  1.  1.  1.  1.\n",
      "  1.  1.  1.  1.  1.  1.  1.  2.  1.  2.  1.  2.  1.  2.  1.  2.  1.  2.\n",
      "  1.  1.  1.  1.  2.  1.  1.  1.  2.  1.  1.  1.  1.  1.  2.  2.  1.  1.\n",
      "  2.  1.  1.  2.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "  1.  1.  2.  1.  1.  1.  2.  1.  1.  2.  1.  1.  1.  2.  1.  1.  2.  1.\n",
      "  2.  1.  2.  1.  1.  2.  1.  1.  1.  1.  2.  1.  1.  1.  1.  2.  1.  2.\n",
      "  1.  1.  1.  2.  1.  1.  1.  2.  1.  1.  1.  2.  2.  1.  2.  1.  1.  2.\n",
      "  1.  1.  1.  1.  2.  1.  1.  2.  1.  1.  1.  1.  1.  1.  1.  1.  2.  1.\n",
      "  1.  1.  2.  2.  2.  1.  2.  2.  1.  1.  1.  1.  1.  1.  1.  1.  1.  2.\n",
      "  1.  1.  1.  1.  1.  2.  1.  1.  1.  2.  2.  1.  1.  1.  2.  1.  1.  2.\n",
      "  2.  2.  1.  2.  1.  1.  2.  1.  1.  1.  1.  1.  1.  2.  1.  1.  1.  2.\n",
      "  2.  1.  1.  1.  1.  1.  2.  1.  1.  2.  1.  1.  1.  2.  1.  1.  2.  1.\n",
      "  2.  1.  2.  2.  1.  2.  1.  1.  2.  1.  1.  1.  2.  1.  1.  2.  2.  2.\n",
      "  2.  2.  1.  2.  1.  2.  1.  1.  2.  1.  1.  2.  2.  1.  1.  1.  1.  1.\n",
      "  1.  1.  2.  1.  2.  1.  1.  2.  1.  2.  1.  1.  2.  2.  1.  1.  1.  2.\n",
      "  2.  2.  2.  2.  2.  1.  1.  2.  2.  2.  1.  1.  1.  2.  1.  1.  2.  2.\n",
      "  1.  1.  2.  1.  1.  1.  2.  1.  1.  2.  2.  1.  2.  1.  1.  2.  1.  1.\n",
      "  1.  2.  1.  2.  2.  1.  1.  1.  1.  2.  2.  1.  2.  1.  1.  2.  1.  2.\n",
      "  2.  2.  1.  2.  2.  2.  1.  1.  2.  1.  1.  1.  1.  2.  1.  1.  1.  1.\n",
      "  1.  1.  2.  1.  1.  1.  1.  1.  2.  1.  1.  2.  1.  1.  1.  1.  1.  1.\n",
      "  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  2.  2.\n",
      "  1.  1.  1.  1.  2.  2.  1.  1.  1.  2.  1.  1.  2.  1.  1.  1.  1.  1.\n",
      "  2.  2.  2.  1.  2.  1.  1.  2.  2.  1.  1.  2.  1.  1.  1.  1.  2.  1.\n",
      "  1.  2.  1.  1.  1.  1.  1.  1.  1.  2.  1.  1.  1.  2.  1.  1.  2.  2.\n",
      "  1.  2.  1.  2.  1.  2.  1.  2.  1.  1.  2.  1.  1.  1.  1.  2.  1.  1.\n",
      "  1.  2.  1.  1.  1.  1.  2.  1.  1.  2.  1.  1.  1.  1.  2.  2.  2.  1.\n",
      "  1.  1.  1.  1.  2.  1.  1.  1.  1.  1.  1.  1.  1.  2.  1.  1.  1.  2.\n",
      "  1.  1.  2.  2.  2.  1.  1.  1.  1.  2.  1.  1.  2.  1.  1.  1.  2.  2.\n",
      "  2.  1.  1.  2.  2.  1.  2.  2.  1.  1.  1.  1.  2.  1.  2.  1.  1.  1.\n",
      "  2.  1.  1.  2.  2.  1.  1.  2.  1.  1.  1.  1.  2.  1.  1.  2.  2.  1.\n",
      "  2.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  2.  1.  1.  1.\n",
      "  1.  1.  2.  2.  1.  2.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  2.\n",
      "  2.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  2.  1.  1.  2.  2.  1.  2.\n",
      "  2.  2.  1.  1.  2.  1.  2.  2.  1.  2.  1.  1.  1.  2.  1.  1.  1.  2.\n",
      "  2.  1.  2.  1.  1.  1.  1.  1.  1.  1.  2.  1.  2.  2.  1.  2.  2.  2.\n",
      "  1.  1.  1.  1.  2.  1.  1.  1.  1.  2.  1.  1.  2.  1.  1.  1.  1.  1.\n",
      "  2.  2.  1.  1.  1.  1.  2.  2.  2.  2.  1.  2.  1.  1.  1.  1.  1.  1.\n",
      "  1.  1.  1.  1.  1.  1.  1.  1.  2.  1.]\n"
     ]
    }
   ],
   "source": [
    "# ** Reading File into Numpy Array **\n",
    "print 'X: ',X\n",
    "print 'Y: ',Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Normalizing to Zero Mean Unit Variance*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mean = X.mean(axis=0)\n",
    "std = X.std(axis=0)\n",
    "X = (X - mean) / std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels selected are:  ['Checking Account Status' 'Duration(Months)' 'Credit History' 'Purpose'\n",
      " 'Credit Amount' 'Savings Accounts(Bounds)' 'Present Employment Since'\n",
      " 'Installment in % of disposable income' 'Personal Status & Sex'\n",
      " 'Other debters & Guaranters' 'Present Residence Since' 'Property'\n",
      " 'Age in Years' 'Other Installment Plans' 'Housing' 'Existing Credits'\n",
      " 'Job' 'Maintance Liable People Count' 'Telephone' 'Foreign Worker']\n"
     ]
    }
   ],
   "source": [
    "print 'Labels selected are: ',labels_ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**POST Normalization, we Have:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalization gives:  [[-1.25456565 -1.23647786  1.34401408 ..., -0.42828957  1.21459768\n",
      "  -0.19601428]\n",
      " [-0.45902624  2.24819436 -0.50342796 ..., -0.42828957 -0.82331789\n",
      "  -0.19601428]\n",
      " [ 1.13205258 -0.73866754  1.34401408 ...,  2.33486893 -0.82331789\n",
      "  -0.19601428]\n",
      " ..., \n",
      " [ 1.13205258 -0.73866754 -0.50342796 ..., -0.42828957 -0.82331789\n",
      "  -0.19601428]\n",
      " [-1.25456565  1.9992892  -0.50342796 ..., -0.42828957  1.21459768\n",
      "  -0.19601428]\n",
      " [-0.45902624  1.9992892   1.34401408 ..., -0.42828957 -0.82331789\n",
      "  -0.19601428]]\n"
     ]
    }
   ],
   "source": [
    "# Post Normalization\n",
    "print 'Normalization gives: ',X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Principal Component Analysis of Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The variance contributed by each PC by order is:  [ 0.12716872  0.0980043   0.0701715 ]\n",
      "MLE Method Parameters Chosen:  3\n"
     ]
    }
   ],
   "source": [
    "# To getter a better understanding of interaction of the dimensions\n",
    "# plot the first three PCA dimensions\n",
    "fig = plt.figure(1, figsize=(18, 10))\n",
    "ax = Axes3D(fig, elev=-150, azim=110)\n",
    "# Replace mle with any number of components desired for analysis\n",
    "\n",
    "PCA_var = PCA(n_components=3)\n",
    "X_reduced =PCA_var.fit_transform(X)\n",
    "print 'The variance contributed by each PC by order is: ',PCA_var.explained_variance_ratio_\n",
    "print 'MLE Method Parameters Chosen: ',PCA_var.n_components_\n",
    "ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=Y)\n",
    "# for Default colormap: Class 0: Blue Class 1: Green Class 2: Red\n",
    "ax.set_title(\"First three PCA directions\")\n",
    "ax.set_xlabel(\"1st eigenvector\")\n",
    "ax.w_xaxis.set_ticklabels([])\n",
    "ax.set_ylabel(\"2nd eigenvector\")\n",
    "ax.w_yaxis.set_ticklabels([])\n",
    "ax.set_zlabel(\"3rd eigenvector\")\n",
    "ax.w_zaxis.set_ticklabels([])\n",
    "ax.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using varying kernels allows for Non-linear manifold representations\n",
    "\n",
    "## Kernel PCA\n",
    "\n",
    "**It's clear from the representation below, that the dataset forms three distinct clusters, that can be segregated using a high-dimensional non-linear classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib qt\n",
    "fig = plt.figure(1, figsize=(18, 10))\n",
    "ax = Axes3D(fig, elev=-150, azim=110)\n",
    "PCA_var= kpca(n_components=14,kernel='poly')\n",
    "X_reduced =PCA_var.fit_transform(X)\n",
    "ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=Y)\n",
    "ax.set_title(\"First three PCA directions\")\n",
    "ax.set_xlabel(\"1st eigenvector\")\n",
    "ax.w_xaxis.set_ticklabels([])\n",
    "ax.set_ylabel(\"2nd eigenvector\")\n",
    "ax.w_yaxis.set_ticklabels([])\n",
    "ax.set_zlabel(\"3rd eigenvector\")\n",
    "ax.w_zaxis.set_ticklabels([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining Confusion Matrix Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class_labels_=np.array(['Low Risk Credit Rating','High Risk Credit Rating'])\n",
    "def plot_confusion_matrix(cm,name,title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(class_labels_))\n",
    "    plt.xticks(tick_marks,class_labels_, rotation=45)\n",
    "    plt.yticks(tick_marks,class_labels_)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TRAINING MODELS BELOW: **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier:  Nearest Neighbors  Accuracy:  0.776666666667\n"
     ]
    },
    {
     "data": {
      "image/png": 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fSet2JlcH9Q2XdE3e3k7SIXn7/yR9uso5R0h6QdIYSQ9K+kYN12lXn6SjJG3W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\n0s20dQgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x33ca748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier:  RBF SVM  Accuracy:  0.8\n"
     ]
    },
    {
     "data": {
      "image/png": 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2PSo/X5vUZHGvJAH9SDX05UhNGy/k7S4C9unAuQ6UtCewNLBlYfnKko4F5iC9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\nYBGIQwihwSIQhxBCg/0/LPzJ/DpIlrQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeab9908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier:  Decision Tree  Accuracy:  0.68\n"
     ]
    },
    {
     "data": {
      "image/png": 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+SZfkD0c17Y5n+6gayxbCgFdPk3i9+cSr7dsTGjY6JdcsD8nP15M0PtcyT5I0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\nMRMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe5d5b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier:  Random Forest  Accuracy:  0.736666666667\n"
     ]
    },
    {
     "data": {
      "image/png": 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bZ9m+oDvHKLOj7dWBPwEnF86zte33unn8c4G3bC9jeziwO2kGtumoc5NJOJfv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\n50VNPYSsEGgWIjXDzGd7SGH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ZKenrnTjXMEkxRUGTiKAeWsWHtte0vQpprvIflG/QyeGF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\n4QBAIEQVfVcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe4587f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier:  Naive Bayes  Accuracy:  0.723333333333\n"
     ]
    },
    {
     "data": {
      "image/png": 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4F/BYlTYn2j5R0rLAvZIutD21k3PM1J/tw2qULQj6PS26ir1xU/6y5Xhg3l5b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\ngiBoIUJpB0EQtBChtIMgCFqI/wclHHlIXy7w3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x85b90f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "\n",
    "filename=\"credit.csv\"\n",
    "f = open(filename)\n",
    "f.readline()  # skip the header\n",
    "data =  np.loadtxt(fname = f, delimiter = ',',dtype='double')\n",
    "Y = data[:,data.shape[1]-1]\n",
    "X = data[:, 0:data.shape[1]-1] \n",
    "\n",
    "names = [\"Nearest Neighbors\", \"RBF SVM\", \"Decision Tree\",\n",
    "         \"Random Forest\", \"Naive Bayes\"]\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(),\n",
    "    SVC(probability=True),\n",
    "    DecisionTreeClassifier(),\n",
    "    RandomForestClassifier(),\n",
    "    GaussianNB()]\n",
    "\n",
    "\n",
    "#Normalizing to Zero Mean Unit variance\n",
    "\n",
    "mean = X.mean(axis=0)\n",
    "std = X.std(axis=0)\n",
    "X = (X - mean) / std\n",
    "\n",
    "\n",
    "# preprocess dataset, split into training and test part\n",
    "# standardize\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=.3)\n",
    "\n",
    "# iterate over classifiers\n",
    "for name, clf in zip(names, classifiers):\n",
    "    clf.fit(X_train, y_train)\n",
    "    score = clf.score(X_test, y_test)\n",
    "    y_predic=clf.predict(X_test)\n",
    "    print 'Classifier: ',name,' Accuracy: ',score\n",
    "    cm=confusion_matrix(y_test,y_predic)\n",
    "    np.set_printoptions(precision=2)\n",
    "    cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "    plt.figure()\n",
    "    plot_confusion_matrix(cm_normalized,name,title='Normalized confusion matrix for '+name)\n",
    "    plt.show()\n"
   ]
  },
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   "source": []
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